A known problem is digital image noise reduction in the face of (a) randomly distributed noise, which is often additive, (b) fixed pattern noise due to imaging detector response non-uniformities, and (c) analog recording noise of video signals due to video standard bandwidth limitations and luminance/chrominance signal formats.
The need for image restoration in the face of noise exists in a wide range of applications such as electronic imaging and scanning, video recording equipment, analog and digital TV displays, and digital image compression. Imaging sensors such as CCD-TV continuous and still cameras and medical imaging systems often face low light level situations, in which the image quality deteriorates due to reduced signal to noise ratios. Significant amplification of such video signals amplifies the various noise effects to the point where they are visible and disturbing to the observer. Electronic noise in still-video images is usually perceived as high frequency noise. In image sequences, electronic noise fluctuates randomly due to its random statistical nature, and can therefore be reduced by temporal integration.
Photo response non-uniformities of imaging detectors, such as CCD imagers, CCD image scanners and image facsimile machines, result in fixed-pattern noise. Its spatial structure depends on the internal design characteristics of the detector. CCD scanner detectors, for example, suffer from fixed-pattern noise caused by nonuniformities in the detector element responsivities. These are only partially correctable using digital calibrated processing schemes, the residual fixed-pattern noise remaining visible.
Fixed-pattern noise is particularly disturbing in still imagery. These effects are usually masked and not visually perceived in high contrast textured images. However, in low light level imaging situations where extensive signal amplification is required in order to perceive low contrasts, the fixed pattern noise effects are clearly visible and disturbing to the observer.
Image noise also appears in medical imaging applications, for example in ultrasound, and in photon-counting imaging systems. Image scanning applications also often require noise reduction, depending on the lighting conditions, and on the type of scanned data (imagery and text on paper or film).
Spatial and spatio-temporal image compression methods such as block transform techniques, often result in two noise artifacts, namely high frequency noise in the vicinity of image edges within each block, and low frequency block noise between adjacent blocks.
Image noise is an important factor which governs the effectiveness of edge detection operations in machine vision applications.
Existing digital image noise reduction techniques can generally be categorized into three classes:
(a) Spatial smoothing operators which utilize only spatial image information for reducing image noise,
(b) temporal image integration operators which prolong the effective exposure time of an image changing over time hence reducing temporal random fluctuations of image noise, and
(c) combinations of the techniques (a) and (b).
Linear spatial smoothing operators, such as low pass filters, usually result in subjectively unacceptable blurring of essential high frequency image detail such as edges, lines and contours. More advanced filtering techniques such as Wiener filters adapt to local estimates of signal and noise according to statistical models of the signal noise processes, which are often difficult to define a-priori. This type of technique is discussed in a document referenced herein as Document 1 of Appendix A.
A Wiener filter is an example of a more general class of filters known as Kalman filters, described in Documents 2 and 4 of Appendix A. Kalman filters require more intensive computation for local estimation of second order statistical parameters in the image. Kalman filtering techniques also rely on signal and noise models which are generally not appropriate for all images.
Other operators, such as median filters, do not require any a-priori knowledge of signal and noise models, and are designed to preserve high frequency edge signals while at the same time reducing the noise in smooth image regions. However, such operators introduce unwanted image noise effects due to the statistical nature of their pixel replication. This type of operator is discussed in Document 3 of Appendix A and is compared there to other edge preserving operators.
Temporal image noise is often reduced by image integration techniques, for example by use of recursive running-average filtering techniques, which are discussed in Documents 4 and 6 of Appendix A. However, in situations where motion occurs in the image, due to camera motion and/or motion of an object in the scene, high frequency image detail is usually compensated and blurred due to the prolonged effective exposure time. Therefore, such methods are unsuitable for many applications.
Two-directional low pass filtering techniques are discussed in Document 5 of Appendix A in the context of dynamic range compression of images.
A theoretical and more general treatment of two-directional filtering of images is provided in Document 6 of Appendix A. However, the described techniques do not provide visually pleasing results.
Heuristic techniques using fuzzy logic formulations have been applied to noise reduction problems with limited success, as explained in Document 7 of Appendix A.